Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

Energy Conversion and Management

Volumn 112, Issue , 2016, Pages 208-219

  Zhang, Yachao ^a   Liu, Kaipei ^a   Qin, Liang ^a   An, Xueli ^b  

^a WUHAN UNIVERSITY   (China)

^b CHINA INSTITUTE OF WATER RESOURCES AND HYDROPOWER RESEARCH   (China)

Author keywords

Combined forecasting model; Probabilistic interval prediction; Quantile regression averaging; Self adaptive multi strategy differential evolution; Variational mode decomposition; Wind power

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRIC POWER GENERATION; ENERGY POLICY; EVOLUTIONARY ALGORITHMS; FORECASTING; LEARNING SYSTEMS; OPTIMIZATION; SIGNAL PROCESSING; TIME SERIES ANALYSIS; WIND POWER; WIND TURBINES;

COMBINED FORECASTING; DIFFERENTIAL EVOLUTION; INTERVAL PREDICTION; MODE DECOMPOSITION; QUANTILE REGRESSION;

WEATHER FORECASTING;

EID: 84955259754     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2016.01.023     Document Type: Article

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